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MODIS/Aqua Sea Ice Extent 5-Min L2 Swath 1km, Version 4

Summary

MODIS/Aqua Sea Ice Extent 5-Min L2 Swath 1km (MYD29) contains the following fields: sea ice by reflectance, sea ice by reflectance pixel quality assurance (QA), ice surface temperature (IST), IST pixel QA, sea ice by IST, combined sea ice, latitudes, and longitudes in HDF-EOS format along with corresponding metadata. Latitude and longitude geolocation fields are at 5 km resolution while all other fields are at 1 km resolution. Version 4 (V004) MYD29 data uses Aqua/MODIS band seven instead of band six. The sea ice algorithm uses a Normalized Difference Snow Index (NDSI) modified for sea ice to distinguish sea ice from open ocean based on reflective and thermal characteristics. The only data available for Version 4 (V004) is the Golden Month, which is a sample of V004 data covering the time period 29 August 2002 (day of year 241) through 7 October 2002 (day of year 280). The Golden Month is only available by special request by contacting NSIDC User Services.

Please note that NSIDC now has a complete series of Version 5 data, which is the highest version number now available and represents the best quality of data.

As a condition of using these data, you must cite the use of this data set using the following citation. For more information, see our Use and Copyright Web page.

The following example shows how to cite these data in a publication. List the principal investigators, year of data set release (2003), data set title and version, date of the version you used, publisher (NSIDC), and digital media.

Overview Table

Coverage is global, but the sea ice algorithm applies only to ocean pixels. Spatial resolution at nadir is approximately 1 km for the data fields and 5 km for the latitude and longitude geolocation fields.

Algorithms that generate sea ice products are continually being improved, as limitations become apparent in early versions of data. As a new algorithm becomes available, a new version of data is released. Users are encouraged to work with the latest version available, which is the highest version number. MYD29 is available in Versions 3 and 4; however. See MODIS Product Versions for the reprocessing history and summary of changes in each version.

Please visit the following sites for more information about known data problems, production schedule, and future plans:

MODIS products are archived in HDF-EOS format, which employs point, swath, and grid structures to geolocate parameters to geographic coordinates. Various software packages, including public domain, support the HDF-EOS data format. See the Software section for more information.

MYD29 is split into three different file types: (1) swaths acquired during daylight, (2) swaths acquired during night, and (3) swaths that were acquired in both day and night. The DayNightFlag object in the MYD29 CoreMetadata.0 global attribute specifies what input was used for a given MYD29 granule. Content of sea ice data products is different between day and night, because MODIS visible data are not acquired when the sensor is observing the surface in darkness. Thermal data are acquired day and night. Swaths acquired during day or that observed a combination of day and night contain fields based on reflective and thermal data. In swaths that were acquired in night mode, only data fields based on thermal data are included. Each data file contains a mix of data fields, depending on whether the data were acquired at night or during the day:

Sea Ice by Reflectance (with HDF-predefined and custom local attributes)

Description of data fields

Sea Ice by Reflectance
The sea ice algorithm identifies pixels as being sea ice, ocean, cloud, land, inland water, or other condition. Sea ice is distinguished from open water based on reflective properties. Results are stored as integer values.

Ice Surface Temperature
IST data are expressed in Kelvins and are stored as scaled integer data in HDF-EOS calibrated form. You must convert data to Kelvins using the calibration data in the HDF predefined local attributes:

IST = 0.01 * (calibrated data - add_offset)

The valid range for IST is 243 to 271.5 K.

Sea Ice by Reflectance PixelQA and Ice Surface Temperature PixelQA
These fields store the quality of the algorithm on a pixel-by-pixel basis. QA information tells if the algorithm results were nominal, abnormal, cloud-obscured, invalid, or if other defined conditions were encountered for a pixel. If all the input data and calculations in the algorithm were nominal for a pixel, the QA bit is set to nominal. If data showed abnormal values, for example out of range, the algorithm proceeds and outputs a value but flags it as abnormal. If the pixel is obscured by cloud, then the bit setting is cloud. If invalid data or calculations result in unacceptable values, the bit setting is invalid. See MODIS Sea Ice Quality Assurance Fields for more information about QA flags in sea ice products.

Sea Ice by ISTA pixel with an IST less than or equal to 271.5 K is classified as sea ice, and any pixel above that threshold is classified as open ocean.

Combined Sea Ice
This field represents the agreement or disagreement between sea ice identified by reflectance characteristics or by estimated IST. Data show pixels that were detected as sea ice in both the Sea Ice by Reflectance and Sea Ice by IST fields, and where the two techniques differed in detection of sea ice. Presence of other features, such as land, is consistent between these two fields.

Latitude and Longitude
Click the following thumbnail to see a larger diagram of how latitude and longitude fields are mapped to the sea ice fields.

The latitude and longitude data correspond to the center pixel of a 5 km by 5 km block of pixels in the sea ice field. Geolocation data are mapped to the sea ice data with an offset value of two and increment value of five. The offset indicates how far to move along a data dimension until reaching the first point with a corresponding entry along the geolocation dimension. The increment tells how many points to travel along the data dimension before the next point is found for which there is a corresponding entry along the geolocation dimension. In this case, the first element 0,0 in the latitude and longitude field corresponds to element 2,2 in the Sea Ice by Reflectance field. The algorithm then increments by five pixels in the cross track or along track direction to map geolocation data to the Sea Ice by Reflectance field elements.

Metadata

A separate ASCII text file containing metadata with a .met file extension accompanies the HDF-EOS file. The metadata file contains some of the same metadata as in the product file, but also includes other information regarding archiving, user support, and post production quality assurance (QA) relative to the granule ordered. The post-production QA metadata may or may not be present depending on whether or not the data granule has been investigated for quality assurance. The metadata file should be examined to determine if post-production QA has been applied to the granule (Riggs, Hall, and Salomonson 2003).

The sea ice algorithm classifies pixels as sea ice, cloud, open ocean, inland water, or land. In the Sea Ice by Reflectance field, sea ice is distinguished from open water based on reflective properties. Sea ice extent is determined by the number of pixels classified as sea ice. In the IST field, pixels classified as sea ice contain an IST value in Kelvins, and pixel values are scaled by 100 for all classes. The IST algorithm was designed for sea ice; however, IST values are provided for areas over open ocean.

Because sea ice varies in concentration from near zero to 100 percent, it can show different reflectances and temperatures within a pixel, due to sub-pixel effects. Sea ice can also have different reflectances depending on snow cover and presence of surface melt monds. Melt ponds and leads in the summer months affect the emissivity of the ice surface and, therefore, the calculation of ice surface temperature. Clouds may obscure sea ice observations, which is a problem when noting the movement of sea ice over an eight-day time series. Small ice floes, polynyas, and leads at subpixel resolution also contribute to errors in identification and mapping of sea ice (Hall et al. 1998).

Accuracy of IST is estimated to be 0.3 to 2.1 Kelvins (Key et al. 1997). MODIS Airborne Simulator (MAS) data and campaign field data are currently used to establish bounds for MODIS IST accuracy.

In MYD29 Version 4 (V004) data, the sea ice algorithm uses Aqua/MODIS band 7. Good quality has been observed in the sea ice maps; however, investigation of effects of the switch to band 7 is continuing. The cloud mask product, MYD35_L2, used as input to the MYD29 algorithm also changed to use of band 7. The effect of that change relative to sea ice/cloud discrimination is being investigated. The IST was not affected by the switch to band 7 except, possibly indirectly by the cloud mask switch to band 7. Validation status is set at provisional until further validation work specific to Aqua IST maps can be completed.

Provisional means that the products are partially validated; incremental improvements are still occurring. These are early science validated products and are useful for exploratory and process scientific studies. Quality may not be optimal since validation and quality assurance are ongoing. Users are urged to review product quality summaries before publication of results.

Analysis of the quality of the sea ice data products is an ongoing activity. Specific information on the science quality of the sea ice data products is reported in the ScienceQualityFlagExplanation object in the CoreMetadata.0 global attribute. The URL for the quality assessment site is given in the product metadata and is linked to from the Warehouse Inventory Search Tool (WIST) when ordering data. The ScienceQualityFlagExplanation is changed in response to analysis and should be checked for updated information. In the MOD29 and MOD29P1D data products there are two instances of the ScienceQualityFlagExplanation, one for sea ice determined by reflectance data and one for IST written in the metadata. Information on both is posted at that URL.

The Ice Surface Temperature PixelQA and the Sea Ice by Reflectance PixelQA data fields provide additional information on algorithm results for each pixel within a spatial context, and are used as a measure of usefulness for sea ice data. QA data are stored as bit flags. QA information is extracted by reading the bits within a byte (See MODIS Sea Ice Quality Assurance Fields). The QA information tells if algorithm results were nominal, abnormal, or if other defined conditions were encountered for a pixel (Riggs, Hall, and Salomonson 2003).

Sea ice is a highly dynamic feature that requires satellite-based remote sensing to better understand its behavior. Newly formed, smooth, thin sea ice is changed by temperature fluctuations, compressive and shear forces, surface currents, and winds. Sea ice usually becomes snow-covered only a few days after formation. As snow melts on sea ice, albedo decreases across all wavelengths. Sea ice has a much higher albedo compared to open ocean. Specific reflective characteristics of sea ice depend on the age of the ice. Snow-covered, opaque, white sea ice, thick first-year ice, and multiyear ice typically show maximum reflectance between 0.4 µm and 0.8 µm, and again at 1.9 µm. Young sea ice has a lower spectral albedo, 10-40 percent, than older sea ice when measured in this spectral range. Sea ice in the process of ablation and formation of melt ponds shows a decrease in reflectance from 0.6 µm to 0.8 µm, followed by a consistent decrease to approximately 1.6 µm. Sea ice reflectance criteria are used to identify snow-covered sea ice and the age of the ice (Hall and Martinec 1985, Hall et al. 1998).

Measurement of IST is useful for determining ice type and estimating radiative and turbulent heat fluxes for large-scale climate studies. IST estimates are used as an additional discriminatory variable for the identification of sea ice cover. Studies of MODIS Airborne Spectrometer (MAS) images in the Beaufort Sea, near St. Lawrence Island, Alaska, show that the surface temperature of water is typically greater than 271.4 Kelvins, while the surface temperature of saline ice is less than 271.4 Kelvins (Hall et al. 1998). These thresholds take into account the emissivity of sea ice. First-year ice has an emissivity of about 0.92, and multiyear ice has an emissivity of about 0.84. The difference in ice emissivities results in a difference in recorded surface temperatures, allowing a researcher to distinguish the relative age of ice and infer relative ice thickness (Hall and Martinec 1985).

The MODIS instrument provides 12-bit radiometric sensitivity in 36 spectral bands, ranging in wavelength from 0.4 µm to 14.4 µm. Two bands are imaged at a nominal resolution of 250 m at nadir, five bands at 500 m, and the remaining bands at 1000 m. A ±55° scanning pattern at 705 km achieves a 2330 km swath, with global coverage every one to two days.

The scan mirror assembly uses a continuously rotating double-sided scan mirror to scan ±55 degrees, driven by a motor encoder built to operate 100 percent of the time throughout the six year instrument design life. The optical system consists of a two-mirror off-axis afocal telescope which directs energy to four refractive objective assemblies: one each for the visible, near-infrared, shortwave-infrared, and longwave-infrared spectral regions (MODIS Web 2001).

MODIS has a series of on-board calibrators that provide radiometric, spectral, and spatial calibration of the MODIS instrument. The blackbody calibrator is the primary calibration source for thermal bands between 3.5 µm and 14.4 µm while the Solar Diffuser (SD) provides a diffuse, solar-illuminated calibration source for visible, near-infrared, and shortwave infrared bands. The Solar Diffuser Stability Monitor (SDSM) tracks changes in the reflectance of the SD with reference to the sun so that potential instrument changes are not incorrectly attributed to changes in this calibration source. The Spectroradiometric Calibration Assembly (SRCA) provides additional spectral, radiometric, and spatial calibration.

MODIS uses the moon as an additional calibration technique and for tracking degradation of the SD by referencing the illumination of the moon since the moon's brightness is approximately the same as that of the Earth. Finally, MODIS deep space views provide a photon input signal of zero, which is used as a point of reference for calibration (MODIS Web 2001).

The objective of the mission is to develop and implement algorithms that map snow and ice on a daily basis, and provide statistics of the extent and persistence of snow and ice over eight-day periods. Data at 500 m resolution enables sub-pixel snow mapping for use in regional and global climate models. A study of subgrid-scale snow-cover variability is expected to improve features of a model that simulates Earth radiation balance and land-surface hydrology (Hall et al. 1998).

The MODIS sensor contains a system whereby visible light from the earth passes through a scan aperture and into a scan cavity to a scan mirror. The double-sided scan mirror reflects incoming light onto an internal telescope, which in turn focuses the light onto four different detector assemblies. Before the light reaches the detector assemblies, it passes through beam splitters and spectral filters that divide the light into four broad wavelength ranges. Each time a photon strikes a detector assembly, an electron is created. Electrons are collected in a capacitor where they are eventually transferred into the preamplifier. Electrons are converted from an analog signal to digital data, and downlinked to ground receiving stations (MODIS Web 2001).

The EOS Ground System (EGS) consists of facilities, networks, and systems which archive, process, and distribute EOS and other NASA earth science data to the science and user community. The EOS Data and Operations System (EDOS) performs forward-link processing of data and return-link of science data from EOS spacecraft and instruments, processes telemetry to generate Level-0 products, and maintains a backup archive of Level-0 products.

GSFC processes Level-1A data from Level-0 instrument packet data, then processes a Level-1B Calibrated Radiance product (MOD02) and Geolocation Fields (MOD03). The MODIS SIPS team creates a Level-2 product for sea ice (MOD29/MYD29), which is then used as input to create Level-3 gridded products for day and night sea ice data (MOD29P1D/MYD29P1D and MOD29P1N/MYD29P1N, respectively). These data are archived at the NSIDC DAAC and distributed to EOS investigators and other users via external networks and interfaces (MODIS Web 2000). Data are available to the public through the WIST.

Data products are generated by the MODIS Science Investigator-led Processing System (SIPS) and transferred to NSIDC. Figure 1 is a flowchart that summarizes the steps in the MODIS sea ice algorithm (Riggs, Hall, and Ackerman 1999), which identifies sea ice on the basis of reflectance characteristics in the visible and near infrared (IR) wavelengths, and also by IST. Algorithm criteria are based on the Normalized Difference Sea Ice Index (NDSI). The NDSI is used to detect the high reflectance of sea ice at visible wavelengths, and the low reflectance at approximately 1.6 µm. NDSI is calculated using MODIS bands 4 (0.55 µm) and 6 (1.6 µm) radiances:

Constraints are applied in the order listed. After they are applied, only pixels having a 95 percent or greater probability of being unobstructed by cloud over an ocean surface are analyzed for sea ice. Clouds are masked with the MODIS Cloud Mask data product (MOD35_L2). Land and inland water bodies are masked with the MODIS 1 km mask contained within the MODIS geolocation product (MOD03).

Intermediate checks for theoretical bounding of reflectance data and the NDSI ratio are made in the algorithm. Reflectance values should be between 0-100 percent, and the NDSI ratio should be within -1.0 to +1.0. Summary statistics are kept for pixels that exceed these theoretical limits; however, the test for sea ice is done regardless. A quality flag is set in the QA data array to indicate the occurrence of sea ice.

Ice surface temperature

A split-window technique is used to determine sea surface temperature and ice surface temperature. This technique allows for correction of atmospheric effects (primarily water vapor). Relatively thin sea ice (less than 10 cm with no snow cover), which has a lower albedo and which may not be detected using the NDSI, is identified using the difference between ice surface and sea surface temperature. If the difference in surface temperature at 11.0 µm and 12.0 µm (MODIS channels 31 and 32, respectively) is small, then the algorithm assumes a clear atmosphere. Given this assumption, the surface temperature may then be estimated directly from the observed pixel brightness value at 11.0 µm, with an adjustment for the surface emissivity (0.985 for sea water). Sea ice is then identified as any pixel with a surface temperature less than or equal to the freezing point of sea water (271.5 K). Accuracy of the IST measurement may be increased by regression of the estimated IST with temperatures modeled from radiative transfer models or with observed surface temperatures (Hall et al. 1998, Riggs, Hall, and Salomonson 2003).

Radiance data from MODIS channels 31 and 32 (11 µm and 12 µm, respectively) are first converted to brightness temperatures with an inversion of Planck's equation (Key et al. 1994):

The following equation, based on the technique of Key et al. (1997), is then used to estimate ice surface temperature (IST). Key's equation originally developed for the Advanced Very High Resolution Radiometer (AVHRR) was adapted for use with MODIS channels 31 and 32.

Cloud masks

The major caveat with the IST algorithm is that it is only applicable to clear-sky conditions. Inadequate cloud masking may result in significant error in estimating the IST. The MODIS cloud mask is used to identify clear sky conditions, since only pixels with a 95 percent or greater probability of being unobstructed by cloud cover will be considered. Water vapor affects the accuracy of the IST calculation, so variable coefficients are used to correct for atmospheric water vapor (Hall et al. 1998, Riggs, Hall, and Salomonson 2003).